<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<title>AMBHAS: /home/tomer/svn/ambhas/ambhas/errlib.py Source File</title>

<link href="tabs.css" rel="stylesheet" type="text/css"/>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
  $(document).ready(initResizable);
</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/javascript">
  $(document).ready(function() { searchBox.OnSelectItem(0); });
</script>

</head>
<body>
<div id="top"><!-- do not remove this div! -->


<div id="titlearea">
<table cellspacing="0" cellpadding="0">
 <tbody>
 <tr style="height: 56px;">
  
  
  <td style="padding-left: 0.5em;">
   <div id="projectname">AMBHAS
   
   </div>
   
  </td>
  
  
  
   
   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
        <span class="left">
          <img id="MSearchSelect" src="search/mag_sel.png"
               onmouseover="return searchBox.OnSearchSelectShow()"
               onmouseout="return searchBox.OnSearchSelectHide()"
               alt=""/>
          <input type="text" id="MSearchField" value="Search" accesskey="S"
               onfocus="searchBox.OnSearchFieldFocus(true)" 
               onblur="searchBox.OnSearchFieldFocus(false)" 
               onkeyup="searchBox.OnSearchFieldChange(event)"/>
          </span><span class="right">
            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
          </span>
        </div>
</td>
   
  
 </tr>
 </tbody>
</table>
</div>

<!-- Generated by Doxygen 1.7.6.1 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
</div>
<div id="side-nav" class="ui-resizable side-nav-resizable">
  <div id="nav-tree">
    <div id="nav-tree-contents">
    </div>
  </div>
  <div id="splitbar" style="-moz-user-select:none;" 
       class="ui-resizable-handle">
  </div>
</div>
<script type="text/javascript">
  initNavTree('errlib_8py.html','');
</script>
<div id="doc-content">
<div class="header">
  <div class="headertitle">
<div class="title">errlib.py</div>  </div>
</div><!--header-->
<div class="contents">
<a href="errlib_8py.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a><a class="code" href="namespaceambhas_1_1errlib.html">00001</a> <span class="comment">#! /usr/bin/env python</span>
<a name="l00002"></a>00002 <span class="comment"># -*- coding: utf-8 -*-</span>
<a name="l00003"></a>00003 <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00004"></a>00004 <span class="stringliteral">Created on Thu Jan 20 15:36:37 2011</span>
<a name="l00005"></a>00005 <span class="stringliteral">@ author:                  Sat Kumar Tomer </span>
<a name="l00006"></a>00006 <span class="stringliteral">@ author&#39;s webpage:        http://civil.iisc.ernet.in/~satkumar/</span>
<a name="l00007"></a>00007 <span class="stringliteral">@ author&#39;s email id:       satkumartomer@gmail.com</span>
<a name="l00008"></a>00008 <span class="stringliteral">@ author&#39;s website:        www.ambhas.com</span>
<a name="l00009"></a>00009 <span class="stringliteral"></span>
<a name="l00010"></a>00010 <span class="stringliteral">A libray with Python functions for calculations of </span>
<a name="l00011"></a>00011 <span class="stringliteral">micrometeorological parameters and some miscellaneous</span>
<a name="l00012"></a>00012 <span class="stringliteral">utilities.</span>
<a name="l00013"></a>00013 <span class="stringliteral"></span>
<a name="l00014"></a>00014 <span class="stringliteral">functions:</span>
<a name="l00015"></a>00015 <span class="stringliteral">    pc_bias : percentage bias</span>
<a name="l00016"></a>00016 <span class="stringliteral">    apb :     absolute percent bias</span>
<a name="l00017"></a>00017 <span class="stringliteral">    rmse :    root mean square error</span>
<a name="l00018"></a>00018 <span class="stringliteral">    mae :     mean absolute error</span>
<a name="l00019"></a>00019 <span class="stringliteral">    bias :    bias</span>
<a name="l00020"></a>00020 <span class="stringliteral">    NS :      Nash-Sutcliffe Coefficient</span>
<a name="l00021"></a>00021 <span class="stringliteral">    L:        likelihood estimation</span>
<a name="l00022"></a>00022 <span class="stringliteral">    correlation: correlation</span>
<a name="l00023"></a>00023 <span class="stringliteral">    </span>
<a name="l00024"></a>00024 <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00025"></a>00025 
<a name="l00026"></a>00026 <span class="comment"># import required modules</span>
<a name="l00027"></a>00027 <span class="keyword">import</span> numpy <span class="keyword">as</span> np
<a name="l00028"></a>00028 
<a name="l00029"></a><a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">00029</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o):
<a name="l00030"></a>00030     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00031"></a>00031 <span class="stringliteral">    this functions removed the data  from simulated and observed data</span>
<a name="l00032"></a>00032 <span class="stringliteral">    whereever the observed data contains nan</span>
<a name="l00033"></a>00033 <span class="stringliteral">    </span>
<a name="l00034"></a>00034 <span class="stringliteral">    this is used by all other functions, otherwise they will produce nan as </span>
<a name="l00035"></a>00035 <span class="stringliteral">    output</span>
<a name="l00036"></a>00036 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00037"></a>00037     data = np.array([s,o])
<a name="l00038"></a>00038     data = np.transpose(data)
<a name="l00039"></a>00039     data = data[~np.isnan(data).any(1)]
<a name="l00040"></a>00040     <span class="keywordflow">return</span> data[:,0],data[:,1]
<a name="l00041"></a>00041 
<a name="l00042"></a><a class="code" href="namespaceambhas_1_1errlib.html#aab46db41db5d488730ef0af3b219c9be">00042</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#aab46db41db5d488730ef0af3b219c9be">pc_bias</a>(s,o):
<a name="l00043"></a>00043     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00044"></a>00044 <span class="stringliteral">    Percent Bias</span>
<a name="l00045"></a>00045 <span class="stringliteral">    input:</span>
<a name="l00046"></a>00046 <span class="stringliteral">        s: simulated</span>
<a name="l00047"></a>00047 <span class="stringliteral">        o: observed</span>
<a name="l00048"></a>00048 <span class="stringliteral">    output:</span>
<a name="l00049"></a>00049 <span class="stringliteral">        pc_bias: percent bias</span>
<a name="l00050"></a>00050 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00051"></a>00051     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00052"></a>00052     <span class="keywordflow">return</span> 100.0*sum(s-o)/sum(o)
<a name="l00053"></a>00053 
<a name="l00054"></a><a class="code" href="namespaceambhas_1_1errlib.html#ae08c6ef2f2c6a2543c32de17a8062b44">00054</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#ae08c6ef2f2c6a2543c32de17a8062b44">apb</a>(s,o):
<a name="l00055"></a>00055     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00056"></a>00056 <span class="stringliteral">    Absolute Percent Bias</span>
<a name="l00057"></a>00057 <span class="stringliteral">    input:</span>
<a name="l00058"></a>00058 <span class="stringliteral">        s: simulated</span>
<a name="l00059"></a>00059 <span class="stringliteral">        o: observed</span>
<a name="l00060"></a>00060 <span class="stringliteral">    output:</span>
<a name="l00061"></a>00061 <span class="stringliteral">        apb_bias: absolute percent bias</span>
<a name="l00062"></a>00062 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00063"></a>00063     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00064"></a>00064     <span class="keywordflow">return</span> 100.0*sum(abs(s-o))/sum(o)
<a name="l00065"></a>00065 
<a name="l00066"></a><a class="code" href="namespaceambhas_1_1errlib.html#a6644b1f966f2f5f2f2e7be6002ccb225">00066</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#a6644b1f966f2f5f2f2e7be6002ccb225">rmse</a>(s,o):
<a name="l00067"></a>00067     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00068"></a>00068 <span class="stringliteral">    Root Mean Squared Error</span>
<a name="l00069"></a>00069 <span class="stringliteral">    input:</span>
<a name="l00070"></a>00070 <span class="stringliteral">        s: simulated</span>
<a name="l00071"></a>00071 <span class="stringliteral">        o: observed</span>
<a name="l00072"></a>00072 <span class="stringliteral">    output:</span>
<a name="l00073"></a>00073 <span class="stringliteral">        rmses: root mean squared error</span>
<a name="l00074"></a>00074 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00075"></a>00075     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00076"></a>00076     <span class="keywordflow">return</span> np.sqrt(np.mean((s-o)**2))
<a name="l00077"></a>00077 
<a name="l00078"></a><a class="code" href="namespaceambhas_1_1errlib.html#acc6d92cecfb3602c020c2b300acf6ce1">00078</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#acc6d92cecfb3602c020c2b300acf6ce1">mae</a>(s,o):
<a name="l00079"></a>00079     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00080"></a>00080 <span class="stringliteral">    Mean Absolute Error</span>
<a name="l00081"></a>00081 <span class="stringliteral">    input:</span>
<a name="l00082"></a>00082 <span class="stringliteral">        s: simulated</span>
<a name="l00083"></a>00083 <span class="stringliteral">        o: observed</span>
<a name="l00084"></a>00084 <span class="stringliteral">    output:</span>
<a name="l00085"></a>00085 <span class="stringliteral">        maes: mean absolute error</span>
<a name="l00086"></a>00086 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00087"></a>00087     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00088"></a>00088     <span class="keywordflow">return</span> np.mean(abs(s-o))
<a name="l00089"></a>00089 
<a name="l00090"></a><a class="code" href="namespaceambhas_1_1errlib.html#a62cd87e9b43a60d0b6ba57fd56a6e411">00090</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#a62cd87e9b43a60d0b6ba57fd56a6e411">bias</a>(s,o):
<a name="l00091"></a>00091     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00092"></a>00092 <span class="stringliteral">    Bias</span>
<a name="l00093"></a>00093 <span class="stringliteral">    input:</span>
<a name="l00094"></a>00094 <span class="stringliteral">        s: simulated</span>
<a name="l00095"></a>00095 <span class="stringliteral">        o: observed</span>
<a name="l00096"></a>00096 <span class="stringliteral">    output:</span>
<a name="l00097"></a>00097 <span class="stringliteral">        bias: bias</span>
<a name="l00098"></a>00098 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00099"></a>00099     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00100"></a>00100     <span class="keywordflow">return</span> np.mean(s-o)
<a name="l00101"></a>00101 
<a name="l00102"></a><a class="code" href="namespaceambhas_1_1errlib.html#aa2a75f914abacee0e008abcb2fcdfe73">00102</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#aa2a75f914abacee0e008abcb2fcdfe73">NS</a>(s,o):
<a name="l00103"></a>00103     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00104"></a>00104 <span class="stringliteral">    Nash Sutcliffe efficiency coefficient</span>
<a name="l00105"></a>00105 <span class="stringliteral">    input:</span>
<a name="l00106"></a>00106 <span class="stringliteral">        s: simulated</span>
<a name="l00107"></a>00107 <span class="stringliteral">        o: observed</span>
<a name="l00108"></a>00108 <span class="stringliteral">    output:</span>
<a name="l00109"></a>00109 <span class="stringliteral">        ns: Nash Sutcliffe efficient coefficient</span>
<a name="l00110"></a>00110 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00111"></a>00111     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00112"></a>00112     <span class="keywordflow">return</span> 1 - sum((s-o)**2)/sum((o-np.mean(o))**2)
<a name="l00113"></a>00113 
<a name="l00114"></a><a class="code" href="namespaceambhas_1_1errlib.html#a87bd160952d99f375f6db2e41e69d013">00114</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#a87bd160952d99f375f6db2e41e69d013">L</a>(s,o, N=5):
<a name="l00115"></a>00115     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00116"></a>00116 <span class="stringliteral">    Likelihood </span>
<a name="l00117"></a>00117 <span class="stringliteral">    input:</span>
<a name="l00118"></a>00118 <span class="stringliteral">        s: simulated</span>
<a name="l00119"></a>00119 <span class="stringliteral">        o: observed</span>
<a name="l00120"></a>00120 <span class="stringliteral">    output:</span>
<a name="l00121"></a>00121 <span class="stringliteral">        L: likelihood</span>
<a name="l00122"></a>00122 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00123"></a>00123     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00124"></a>00124     <span class="keywordflow">return</span> np.exp(-N*sum((s-o)**2)/sum((o-np.mean(o))**2))
<a name="l00125"></a>00125 
<a name="l00126"></a><a class="code" href="namespaceambhas_1_1errlib.html#a90d7ed508dc9771874d835ff6d1d916c">00126</a> <span class="keyword">def </span><a class="code" href="namespaceambhas_1_1errlib.html#a90d7ed508dc9771874d835ff6d1d916c">correlation</a>(s,o):
<a name="l00127"></a>00127     <span class="stringliteral">&quot;&quot;&quot;</span>
<a name="l00128"></a>00128 <span class="stringliteral">    correlation coefficient</span>
<a name="l00129"></a>00129 <span class="stringliteral">    input:</span>
<a name="l00130"></a>00130 <span class="stringliteral">        s: simulated</span>
<a name="l00131"></a>00131 <span class="stringliteral">        o: observed</span>
<a name="l00132"></a>00132 <span class="stringliteral">    output:</span>
<a name="l00133"></a>00133 <span class="stringliteral">        correlation: correlation coefficient</span>
<a name="l00134"></a>00134 <span class="stringliteral">    &quot;&quot;&quot;</span>
<a name="l00135"></a>00135     s,o = <a class="code" href="namespaceambhas_1_1errlib.html#aa94c39600b2ee91026fe817effd372b9">filter_nan</a>(s,o)
<a name="l00136"></a>00136     <span class="keywordflow">return</span> np.corrcoef(o, s)[0,1]
<a name="l00137"></a>00137 
</pre></div></div><!-- contents -->
</div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
     onmouseover="return searchBox.OnSearchSelectShow()"
     onmouseout="return searchBox.OnSearchSelectHide()"
     onkeydown="return searchBox.OnSearchSelectKey(event)">
<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(4)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(5)"><span class="SelectionMark">&#160;</span>Variables</a></div>

<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0" 
        name="MSearchResults" id="MSearchResults">
</iframe>
</div>

  <div id="nav-path" class="navpath">
    <ul>
      <li class="navelem"><a class="el" href="errlib_8py.html">errlib.py</a>      </li>

    <li class="footer">Generated on Sat Jul 21 2012 12:26:08 for AMBHAS by
    <a href="http://www.doxygen.org/index.html">
    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.7.6.1 </li>
   </ul>
 </div>


</body>
</html>
